Optical fibre sensor system

ABSTRACT

An optical fiber sensor system and a method for determining a location of a disturbance having a signal processor with a plurality of activation cells adapted to react to components of a back-scattered signal and label the disturbance.

CROSS-REFERENCES TO RELATED APPLICATIONS

This disclosure is a continuation of U.S. patent application Ser. No.15/305,073, filed on Oct. 18, 2016, which is a national stage entry,filed under 35 U.S.C. § 371, of International Application No.PCT/EP2015/058522, filed on Apr. 20, 2015, which claims the benefit ofand priority to U.K. Patent Application No. 1407011.4, filed on Apr. 18,2014. The contents of the above-noted patent documents are incorporatedherein by reference in their entireties and for all purposes.

BACKGROUND OF THE INVENTION

Field of the Invention

The invention relates to an optical fibre sensor system and a method fordetermining a location of a disturbance.

Introduction to the Invention

Oil, water, gas and other product pipelines form a critical network inevery part of the world and are an easy target for intruders. Thepipelines are also susceptible to earthquakes, tsunamis and to othergeohazard incidents. Monitoring the pipelines in order to keep thepipelines safe from damage is a major challenge. The long distances,often through remote and hostile territory, make the costs of mostconventional monitoring systems prohibitive. If an oil or gas pipelineis damaged it can have devastating impacts on human life and health dueto explosions, fire and contamination; environment due to poisoning offlora and fauna, as well as the associated financial losses and damageto both image and reputation.

The pipelines are susceptible to various types of third-partyinterference, such as deliberate acts (illegal tapping, sabotage) orunintended disruption (construction work, farming). This third-partyinterference can cause huge financial and environmental damage and lossof reputation to the pipeline operators. A reliable, real-time pipelinemonitoring system is therefore required in order to detect events ofinterest and thereby protect nature, human health and economicinterests.

According to a survey in 2009, about 36% of all worldwide pipeline leakswere caused by third party interference (TPI), such as illegal tapping,sabotage or construction work. Examples of the events of interest due tothird party interference include: 4 Oct. 2001 Fairbanks Ak. (USA), 990 toil due to sabotage; 2000 Tschernigow, Ukraine, loss of 500,000 litresdiesel due to illegal tapping; 10 Jun. 1999 Bellingham, Whatcorn Creek,Wash. (USA), 880 t petrol due to construction work, financial damage USD45 million.

Illegal tapping is a major problem in emerging countries like India,China and South America. In 2011 for example, Petroleos Mexicanos(PEMEX) counted 1,324 cases of illegal tapping in Mexico. Every dayPemex estimates a loss of 40,000 litres of oil and gas, which sums up toan annual damage of more than 1 billion US$ of loss. Such damage couldbe avoided or substantially reduced, if third party interference wasdetected before or close to the occurrence of the interference.

Many times the pipeline monitoring is done by walking, driving andflying along the pipeline. The annual costs for walking and driving theline vary from €100 to €350 per kilometre. The additional costs forflying the line amount to €4.50 per kilometre. Usually two inspectionsare performed per month, which adds up to €108 per kilometre (€4.50/km×2flights/month×12 months). The annual costs are between €208 and €458 perkilometre.

Description of the Related Art

A number of systems for the sensing of an acoustic disturbance areknown. The acoustic disturbance can be representative of damage to thepipelines through third party interference or geohazards, as describedin the introduction, or potential damage due to approaching vehicles orindividuals intending to damage the pipeline. These systems involve theuse of an optical fibre laid alongside the pipeline, which acts as asensor and detects changes in the pattern of back-scattered radiation inorder to sense an acoustic disturbance. For example, Pimon GmbH, Munich,Germany, sells an apparatus PMS2500-vibrO that utilizes distributedfibre optical sensing technology to detect the acoustic disturbance. ThePMS2500-vibrO system combines an optical time domain reflectometer(OTDR) with an analysis and pattern recognition software and offers acustomized interface with geographic information system (GIS) mapping.

International Patent Applications No WO 2011/05813, WO 2011/015812 andWO 2011/059501 (QinetiQ) all teach various aspects of using adistributed fibre optic sensing system for establishing events ofinterest. Similarly UK Patent Application No GB 2 491 658 also teaches amethod and system for locating an acoustic disturbance. These patentapplications all have in common that the systems analyse theback-scattered radiation from the optical fibre to establish the eventof interest. Such systems are useful in determining an event of interestfrom the acoustic disturbance, but the systems are known to produce“false positives” in which events are identified that are of no interestand fail to identify some events of interest, in particular when suchevents of interest have not been seen before.

One solution to the issue of incorrect identification of events would beto use an artificial neural network (ANN) to train the system torecognise the event of interest. The ANNs are computational models andare inspired by animal central nervous systems, in particular the brain,that are capable of machine learning and pattern recognition. The ANNsare usually presented as a system of nodes or “neurons” connected by“synapses” that can compute values from inputs, by feeding informationfrom the inputs through the ANN. The synapses are the mechanism by whichone of the neurons passes a signal to another one of the neurons.

One example of the ANN is for the recognition of handwriting. A set ofinput neurons may be activated by pixels in a camera of an input imagerepresenting a letter or a digit. The activations of these input neuronsare then passed on, weighted and transformed by some function determinedby a designer of the ANN to other neurons, etc. until finally an outputneuron is activated that determines which character (letter or digit)was imaged. ANNs have been used to solve a wide variety of tasks thatare hard to solve using ordinary rule-based programming, includingcomputer vision and speech recognition.

There is no single formal definition of an ANN. Commonly a class ofstatistical models will be termed “neural” if the class consists of setsof adaptive weights (numerical parameters that are tuned by a learningalgorithm) and are capable of approximating non-linear functions of theinputs of the statistical models. The adaptive weights can be thought ofas the strength of the connections (synapses) between the neurons.

The ANNs have to be trained in order to produce understandable results.There are three major learning paradigms: supervised learning,unsupervised learning and reinforcement learning.

In a supervised learning, the learning paradigms all have in common thata set of pre-analysed data, for example a waveform, is analysed by theANN and the weights of the connections (synapses) between the neurons inthe ANN are adapted such that the output of the ANN is correlated with aknown event. There is a cost involved in this training. An improvementin the efficiency of the results of the ANN can be obtained by using agreater number of data items representing the known event in a trainingset. The greater number of data items requires, however, an increase incomputational power and time for the analysis in order to get thecorrect results. There is therefore a trade-off that needs to beestablished between the time taken to train the ANN and the accuracy ofthe results.

Recent developments in the ANNs involve so-called ‘deep learning’. Deeplearning is a set of algorithms that attempt to use layered models ofinputs. Jeffrey Heaton, University of Toronto, has discussed deeplearning in a review article entitled ‘Learning Multiple Layers ofRepresentation’ published in Trends in Cognitive Sciences, vol. 11, No.10, pages 428 to 434, 2007. This publication describes multi-layerneural networks that contain top-down connections and training of themultilayer neural networks one layer at a time to generate sensory data,rather than merely classifying the data.

Neuron activity in the prior art ANNs is computed for a series ofdiscrete time steps and not by using a continuous parameter. Theactivity level of the neuron is usually defined by a so-called “activityvalue”, which is set to be either 0 or 1, and which describes an ‘actionpotential’ at a time step t. The connections between the neurons, i.e.the synapses, are weighted with a weighting coefficient, which isusually chosen have a value in the interval [−1.0, +1.0]. Negativevalues of the weighting coefficient represent “inhibitory synapses” andpositive values of the weighting coefficient indicate “excitatoryvalues”. The computation of the activity value in the ANNs uses a simplelinear summation model in which weighted ones of some or all of theactive inputs received on the synapses at a neuron are compared with a(fixed) threshold value of the neuron. If the summation results in avalue that is greater than the threshold value, the following neuron isactivated.

One example of a learning system is described in international patentapplication No. WO 1998 027 511 (Geiger), which teaches a method ofdetecting image characteristics, irrespective of size or position. Themethod involves using several signal-generating devices, whose outputsrepresent image information in the form of characteristics evaluatedusing non-linear combination functions.

International patent application No. WO 2003 017252 relates to a methodfor recognizing a phonetic sound sequence or character sequence. Thephonetic sound sequence or character sequence is initially fed to theneural network and a sequence of characteristics is formed from thephonetic sequence or the character sequence by taking into considerationstored phonetic and/or lexical information, which is based on acharacter string sequence. The device recognizes the phonetic and thecharacter sequences by using a large knowledge store having beenpreviously programmed.

An article by Hans Geiger and Thomas Waschulzik entitled ‘Theorie andAnwendung strukturierte konnektionistische Systeme’, published inInformatikFachreichte, Springer-Verlag, 1990, pages 143-152 alsodescribes an implementation of a neural network. The neurons in the ANNof this article have activity values between zero and 255. The activityvalues of each one of the neurons changes with time such that, even ifthe inputs to the neuron remain unchanged. The output activity value ofthe neuron would change over time. This article teaches the concept thatthe activity value of any one of the nodes is dependent at least partlyon the results of earlier activities. The article also includes briefdetails of the ways in which system may be developed.

The ANNs of the prior art need to be trained using patterns indicativeof events of interest. They may then be good at recognising such “known”events of interest, but can fail if the event is unknown which will leadto a pattern of back-scattered radiation in the fibre optic that isunknown. The ANNs also rely on the events of interest being correctlyprogrammed and identified by an expert and also that the selection ofweighted factors to determine the event of interest is also known.

One further type of event that is difficult to detect with currentsystems is an event that is best identified from a sequence of events.Suppose, for example, that a group of individuals wish to tap an oilpipeline in order to take some of the oil. The sequence of events willprobably involve an excavator approaching the pipeline and possiblegoing backwards and forwards over the pipeline before stopping andputting out its supporting feet to stabilise the excavator. Theexcavator begins to excavate a large hole near the pipeline followed bymanual digging by a group of, for example, three to five people near tothe pipeline. Subsequently, the excavator will be driven off and a valveplaced in the pipeline to remove the oil. A tanker arrives to remove thetapped oil. Each of these individual events may in themselves not be ofconcern (although the manufal digging near the pipeline may beindicative of an attempt to steal oil). However, the entire sequence ofevents will be highly relevant. There is therefore a need for a systemthat is able to combine the signals from all of the events in order toidentify the attempt to steal the oil.

In some instances, the valve is left in place and re-used to tap theoil. This will lead to a different set of detected events. If thelocation can be correlated with a location in which an unusual set ofevents had previously been detected, then there is a need to provide anurgent warning that a further attempt at extracting oil is being made.

SUMMARY OF THE INVENTION

The principal of the method and apparatus of determining locations ofdisturbances or events of interest as described in this disclosure isbased upon analysing back-scattered radiation from an optical fibreusing a signal processor formed from a so-called biologically inspiredneural network (BNN). The activity of any one of the neurons in the BNNis simulated as a biophysical process. The basic neural property of theneuron is a “membrane voltage”, which in (wet) biology is influenced byion channels in the membrane. The action potential of the neuron isgenerated dependent on this membrane voltage, but also includes astochastic (random) component, in which only the probability of theaction potential is computed. The action potential itself is generatedin a random manner. The membrane has in biology some additionalelectro-chemical property affects, such as absolute and relativerefractory periods, adaptation and sensitization that are automaticallyincluded in the BNN of this disclosure.

The basic information transferred from one of the neurons to another oneof the neurons is not merely the action potential (or firing rate, aswill be described later), but also a time dependent pattern of theaction potentials. This time-dependent pattern of action potentials isdescribed as a single spike model (SSM). This means that the interactionbetween inputs from any two of the neurons is more complex than a simplelinear summation of the activities.

The connections between the neurons (synapses) may have different types.The synapses are not nearly just excitatory or inhibitory (as is thecase with an ANN), but may have other properties. For example, thetopology of a dendritic tree connecting the individual neurons can alsobe taken into account. The relative location of the synapses from thetwo of the input neurons on a dendrite in the dendritic tree may alsohave a large influence on the direction between the two neurons.

In addition to the signal processer, the optical fibre sensor system ofthis disclosure comprises an optical fibre with a radiation source forlaunching radiation into the optical fibre and a detector for detectinga back-scattered radiation, which is back-scattered from the opticalfibre.

In one aspect of the disclosure, the radiation source produces infraredlight in a series of optical pulses.

The disclosure also teaches a method for determining a location of adisturbance or event of interest, which comprises launching radiationinto the optical fibre and detecting the back-scattered radiation fromthe optical fibre. The back-scattered radiation is processed to producea plurality of values and this plurality of values is then passed intothe signal processor for analysis. The signal processer accepts thevalues at a plurality of first-activation cells and triggers firstoutputs from the first activation cells. The first outputs are passed toat least a subset of the second activation cells and thereafter secondoutputs are triggered from this subset of the second activation cells.The second outputs from the plurality of subsets of the secondactivation cells are summed in order to produce an output value relatingto the disturbance. The disturbance is identified by reference of theoutput value to a pattern of previously stored back-scattered radiationrepresenting sound profiles of known events.

The signal processor of the disclosure is self-learning and is able tolearn and combine different “noises” of the one and same event or from arelated sequence of events. The signal processor combines and comparesdifferent noise prints and their specific characteristics and soundlevels in order to differentiate between a normal disturbance, such asfield work of farmers, from an event of interest representing a threat,like someone approaching a pipeline with an excavator.

The optical fibre sensor system taught in this disclosure is able toprovide: alert in case of emergency with an accurate GIS location,identify threats in real time in order to reduce the risk from leakagescaused by digging, drilling, tapping, sabotage, earthquake etc., monitoraround 50 km of pipeline with one measuring unit without any additionalpower supply and with scalability to cover deployments of any length byadding additional units. The system reduces false or irrelevant alarmsand has been found to differentiate multiple events down to one meterresolution, as well as track potential intruders, moving in differentvelocities (vehicles or on foot) along the pipeline. The system candetect direction and speed of the possible intruders.

DESCRIPTION OF THE FIGURES

FIG. 1 shows an example of an optical fibre sensor used in the system ofthe disclosure.

FIG. 2 shows a method for detection of a disturbance.

FIG. 3 shows a diagram of the signal processor.

DETAILED DESCRIPTION OF THE INVENTION

The invention is described on the basis of the drawings. It will beunderstood that the embodiments and aspects of the invention describedherein are only examples and do not limit the protective scope of theclaims in any way. The invention is defined by the claims and theirequivalents. It will be understood that features of one aspect orembodiment of the invention can be combined with a feature of adifferent aspect or aspects and/or embodiments of the invention.

The optical fibre sensor system of this disclosure is based ondistributed fibre optical sensing and integrates an optical time domainreflectometer (OTDR) with detection software, as well as a customizedinterface with geographic information system (GIS) mapping. The fibreoptic cables are often already buried near to or attached to thepipeline for telecommunication purposes. The system utilizes a standardsingle mode telecommunication fibre as a listening device for events ofinterest.

The optical time-domain reflectometer (OTDR) is an optoelectronicinstrument used to characterize optical fibres. The reflectometerinjects a series of optical pulses into the optical fibre that is beingtested and detects at the same fibre end the returning light that hasbeen reflected or back-scattered from points along the fibre. The OTDRproduces a so-called reflectogram from this measurement and performs anefficient, precise and wide analysis of the fibre characteristics.

A laser diode launches radiation in the form of the series of opticalpulses into one end of the optical fibre and a photodiode measures thereturning light at the same fibre end. The photodiode is part of theOTDR and produces a signal. The returning light comprises reflectedlight and back-scattered light. The signal detected by the photodiodeprovides relevant information about the events of interest along theoptical fibre. The signal is influenced by irregularities, which theinjected optical pulse experiences on its way along the optical fibre.If a certain area within the optical fibre has an attenuation orreflection, for instance caused by a bending or a fibre connector, thisattenuation or reflection is detected by differences in the returningsignals. The velocity of the optical pulse is known and the exactlocation of the irregularity can therefore be determined on the basis ofthe time difference between the injection of the optical pulse and theincidence of the returning signal. The irregularity may indicate anevent of interest or other disturbance.

Suppose now that a disturbance or event of interest occurs near theoptical fibre. There may be pressure on the optical fibre which willcause at least a small degree of bending of the optical fibre orvibrations/sound waves will briefly change the structure of the opticalfibre and these will result in a change of the back-scattered signal.This change can be detected in the OTDR. Suppose that the disturbance isa moving object, then the slight bending of the optical fibre willchange location as the moving object moves, enabling the speed anddirection of movement of the disturbance to be established.

The optical fibres make a good sensor, as the optical fibres are able tomeasure disturbances over long distances. The back-scattering of thesignals in the optical fibres change on vibrations (e.g. caused byvehicles, footsteps, digging, drilling), on temperature alterations(e.g. caused by escaping pressurized gas), or when optical fibres getstrained, bent, kinked, or cut off.

EXAMPLE

FIG. 1 shows an example of the optical fibre sensor system using theteachings of this disclosure. An optical fibre 20 used in this sensorsystem can be a specially laid optical fibre 20, which has been placedin a region of interest. The optical fibre 20 can also be a standardtelecommunications optical fibre, which generally carries data. Theoptical fibre 20 can be laid near or directly adjacent to a pipeline 25,as outlined in the introduction to the description. Generally theoptical fibre 20 is placed together with other optical fibres in acable. The optical fibre sensor system of this disclosure uses one ofthe optical fibres in the cable as a dark optical fibre, i.e. unused. Atthe other end of the optical fibre 20, an optical terminator should bechosen in one aspect of the invention to avoid reflection from the fibreend.

The optical fibre 20 is connected to a radiation source 30, which in onenonlimiting aspect of the invention is a semiconductor laser producingradiation 35 comprising a series of optical pulses at 1.55 μm wavelengthand launches the radiation 35 into the optical fibre 20 at repetitionrates of, for example, 4 kHz to detect an event of interest at 25 km.Higher repetition rates can be used to detect closer events of interest.There should be only one optical pulse at one time in the optical fibre20 and therefore the repetition rate can be adjusted accordingly.

The returning back-scattered signal 37 from the optical fibre 20 can bedetected by means of a detector 40 at the same end of the optical fibre20 and is converted into a reflectogram in which the time delay ofsignal incidence is shown as distance to the event of interest on theoptical fibre 20. A highly coherent laser as the radiation source 30 isused in order to increase the sensitivity. Due to this high level ofcoherence, an interference pattern can be observed in the back-scatteredsignal 37 (fingerprint). Vibrations cause temporary changes in thestructure of the optical fibre 20 and result in temporary changes inthis interference pattern. The temporary changes in the interferencepattern can be used to detect a disturbance 45, for example third-partyinterference caused by manual or machine digging. Every kind of incidentcauses typical sound waves and changes the back-scattered signals 37leading to differences in the interference pattern. The different kindsof sound waves have characteristics depending on the type of incidentand the properties of the ground through which the sound waves aretransmitted. The back-scattered signals 37 are received and the signalpatterns are digitalized. The signal from the detector 40 passes throughto a signal processor 50. The signal processor 50 is able to classifythe disturbance 45 and send an alarm to a system operator, if required.

The signal processor 50 is able to separate all “regular” sounds frompossible disturbances 45 in the back-scattered signal as well asinoffensive or other irrelevant incidents. The signal processor 50 isalso able to recognise a sequence of disturbances 45, which inthemselves would be considered to be a non-critical incident, but whencombined together lead to a conclusion that the disturbance may be morecritical.

FIG. 3 shows a first example of the signal processor 50 of thedisclosure. The signal processor 50 is connected to the detector 40. Thesignal processor 50 digitises the reflectogram and, for example, aFourier transformation, Laplace filter, autocorrelation or other methodsare applied to produce a plurality of first inputs 132.

The first inputs 132 are passed to a plurality of first activation cells130. The first activation cells 130 are connected in a one-to-onerelationship with the first inputs 132 or a one-to-many relationshipwith the first inputs 132. In other words, ones of the first activationcells 130 are connected to one or more of the first inputs 132. Thenumber of connections depends on the number of the inputs 120, forexample the number of values generated from the interference pattern,and the number of the first activation cells 130

The first activation cells 130 have a first output 137, which comprisesa plurality of spikes emitted at an output frequency. In “rest mode”,i.e. with no signal from the detector 40 on the first inputs 132, thefirst activation cells 130 produce the plurality of spikes at anexemplary output frequency of 200 Hz. The first activation cells 130 aretherefore an example of a single spike model. The application of thesignal derived from the back-scattered signal 37 on the first input 132increases the output frequency depending on the strength of theback-scattered signal 37 from the detector 40 and is, for example, up to400 Hz. The change in the output frequency is substantially immediate onthe application and removal of the components of the back-scatteredsignal 37 at the first input 132, in one aspect of the invention. Thus,the first activation cells 130 react to changes in the back-scatteredsignal 37 almost immediately.

The plurality of first activation cells 130 are connected in amany-to-many relationship with a plurality of second activation cells140. For simplicity only the connection between one of the secondactivation cells 140 and an exemplary number of the first activationcells 130 is shown in FIG. 3 The first outputs 137 from the connectedones of the first activation cells 130 are summed over a time period atthe connected second activations cell 140.

The values of the first outputs 137 are also combined such that thefirst outputs 137′ from (in this case) the three central firstactivation cells 130 are added, whilst the outputs 137″ from the outerones of the first activation cells 130 are subtracted from the totaloutput 137. In other words the central three inputs 132 contributepositively to the signal received at an input 142 of the secondactivation cell 140, whilst the signals from the outer sensors 132″ aresubtracted. It will be appreciated that the aspect of three centralfirst activation cells 130 and the outer ones of the first activationcells 130 is merely an example. A larger number of first activationcells 130 can be used.

The outputs 137′ and 137″ are merely one example of the manner in whichthe outputs 137 can be combined in general. It was explained in theintroduction to the description, that the connections (synapses) betweenthe neurons or activation cells are not generally combined in asummation model, but have a stochastic component. This stochastic aspectof the invention in which first activation cells 130 connected to thedetector 40 and to the second activation cells 140 is merely one aspectof the invention. The connections can be modified as appropriate.

The second activation cells 140 have different activation levels andresponse times. The second activation cells 140 also produce spikes at afrequency and the frequency increases dependent on the frequency of thespikes at input signal 142. There is no one-to-one relationship betweenthe output frequency of the second activation cells 140 and the inputfrequency of the input signal 142. Generally the output frequency willincrease with an increase of the input signal 142 and saturates at athreshold value. The dependency varies from one of the second activationcells 140 to another one of the second activation cells 140 and has astochastic or random component. The response time of the secondactivation cells 140 also varies. Some of the second activation cells140 react almost immediately to a change in the input signal 142,whereas other ones require several time periods before the secondactivation cells 140 react. Some of the second activation cells 140 areturned to rest and issue no second output signal 147 with increasedspike frequency when the input signal 142 is removed, whereas other onesremain activated even if the input signal 142 is removed. The durationof the activation of the second activation cell 140 thus varies acrossthe plurality of activation cells 140. The second activation cells 140also have a ‘memory’ in which their activation potential depends onprevious values of the activation potential. The previous values of theactivation potential are further weighted by a decay-factor, so thatmore recent activations of the second activation cell 40 affects theactivation potential more strongly than all the ones.

The second outputs 147 are passed to a plurality of third activationcells 170 arranged in a plurality of layers 180. Each of the pluralityof layers 180 comprise a middle layer 185, which is connected to thesecond outputs 147 and one or more further layers 187, which areconnected to third activation cells 170 in other ones of the layers 187.In the example of figure one only five layers 180 are shown, but this ismerely illustrative. In one aspect of the invention for the recognitionof the reflectogram, seven layers are present. It would be equallypossible to have a larger number of layers 180, but this would increasethe amount of computing power required.

The second outputs 147 are connected in a many-to-many relationship withthe second activation cells 40.

The third activations cells 170 also have different activation levelsand different activation times as discussed with respect to the secondactivation cells 140. The function of the second activation cells 140 isto identify features in the back-scattered signal 37 whereas thefunction of the third activation cells 170 is to classify thecombination of the features.

The third activation cells 170 in one of the layers 180 are connected ina many-to-many relationship with third activation cells 170 in anotherone of the layers 180. The connections between the third activationcells 170 in the different layers 180 are so arranged that some of theconnections are positive and reinforce each other, whilst other ones ofthe connections are negative and diminish each other. The thirdactivation cells 170 also have a spike output, the frequency of which isdependent on the value of their input.

There is also a feedback loop (265 in FIG. 3) between the output of thethird activation cells 170 and the second activation cells 140, whichserves as a self-controlling mechanism. The feedback between the thirdactivation cells 170 and the second activation cells 140 is essentiallyused to discriminate between different features in the reflectogram andto reduce overlapping information. This is done by using the feedbackmechanism to initially strengthen the second activation cells 140relating to a particular feature in the reflectogram to allow thatfeature to be correctly processed and identified. The feedback thenreduces the output of the second activation cells 140 for the identifiedfeature and strengthens the value of the second activation cells 140related to a further feature. This further feature can then beidentified. This feedback is necessary in order to resolve anyoverlapping features in the reflectogram, which would otherwise resultin an incorrect classification.

The processor 50 further includes an input device 190 that is used toinput information items 195 relating to the reflectogram. Theinformation items 195 may include a name or a label generally attachedto the reflectogram and/or to one or more features in the reflectogram.The input device 190 is connected to a data processor 200 having amemory and which also accepts the third outputs 77. The signal processor200 compares the third outputs 177 relating to a particular storedfeature in the reflectogram with the inputted information items 195 andcan associate the particular reflectogram (or a portion thereof) withthe inputted information items 195. This association is memorized sothat if an unknown pattern or feature in the reflectogram is detected bythe detector 40 and the third outputs 177 are substantially similar tothe association, the signal processor 200 can determine that unknownpattern is in fact a known pattern and output the associated item ofinformation 195 to the user at an output 198.

The pattern recognition system can be trained to recognize a largenumber of patterns in the reflectogram using an unsupervised leaningprocess. These patterns will produce different ones of the third outputs177 and the associations between the information items 195 and thepatterns are stored. This unsupervised learning process is carried outusing a set of pre-stored patterns representing features of interestfrom existing reflectograms and running the pre-stored patterns throughthe processor 50 to determine the third outputs 177. The third outputs177 are then associated with the information items 195 in the signalprocessor 200.

The system and method of the current disclosure can be used to determineand classify unknown disturbances 45, as shown in FIG. 2.

In this example of the system and method the radiation 35 is launched instep 205 into the optical fibre 20. The detector 40 receives in step 210the back-scattered signal 37 and passes in step 235 the back-scatteredsignal 37 in step 220 to the processor 50. In step 230, theback-scattered signal 37 in the reflectogram is digitalised and brokeninto components and passed in step 235 to the first inputs 132. Thecomponents trigger the first activation cells 130 and the degree oftriggering in step 240 depends on the strength of the components.

The first outputs 137 from the first activation cells 130 aretransferred in step 250 to the second activation cells 140 and then tothe third activation cells 170 in step 260. The activation potential ofthe first activation cells 130 depends upon the strength of thecomponents in the back-scattered signal 37. These components aretransferred into the lower levels and initially an apparently randomsequence of third activation cells 80 appears to be fired. The firingstabilises after a certain period of time and “structures” are createdwithin the plurality of layers 180, which reflect the features in theback-scattered signal 37.

A label 195 can be associated with the one or more features in theback-scattered signal. The structure within the plurality of layers 180corresponds therefore to the feature. The label 195 will be input by theinput device 190, such as a keyboard

The procedure is repeated for a different feature. This differentfeature creates a different structure within the plurality of layers180. The learning procedure can then proceed using different ones of thefeatures.

Once the learning is complete, an unknown feature in the back-scatteredsignal 37 can be detected. This unknown feature generates first inputs132 in the first activation cells 130 in step 210 which are transferredto the second activation cells 40 in step 250 to identify the featuresand then in step 260 into the plurality of layers 180 to enableclassification of the feature. The signals in the plurality of layers180 can be analysed and the structure within the plurality of layers 180most corresponding to the unknown feature is deduced in step 280. Thesystem can therefore output the label associated with the feature. Theunknown feature is therefore identified.

Should the system be unable to identify the feature, because a new typeof structure has been created in the plurality of layers 180, then thesystem can give an appropriate warning and human intervention can beinitiated in order to review the disturbance and classify the unknownfeature or to resolve in the other conflicts.

The feedback in step 265 between the second activation cells 140 and thethird activation cells 170 can be easily understood by considering twooverlapping elements in the feature. Initially the first activationcells 130 will register the difference in the feature around theoverlapping elements, but cannot discriminate the type of feature, i.e.separate out the two different features in the overlapping elements.Similarly adjacent ones of the second activation cells 140 will beactivated because of the overlapping nature of the two overlappinglines. If all of the second activation cells 140 and the thirdactivation cells 170 reacted identically, then it would be impossible todiscriminate between the two overlapping features. It was explainedabove, however, that there is a random or stochastic element to theactivation of the second activation cells 140 and to the thirdactivation cells 170. This stochastic element results in some of thesecond activation cells 140 and/or the third activation cells 170 beingactivated earlier than other ones. The mutual interference between thesecond activation cells 40 or the third activation cells 170 willstrengthen and/or weaken the activation potential and thus those secondactivation cells 140 or third activation cells 170 reacting to one ofthe overlapping elements will initially mutually strengthen themselvesto allow the feature to be identified. The decay of the activationpotential means that after a short time (milliseconds) those secondactivation cells 40 or third activation cells 170 associated with theidentified overlapping feature diminish in strength and the other secondactivation cells 140 or other third activation cells 170 relating to theas yet unidentified overlapping element are activated to allow this oneof the overlapping element to be identified.

In another aspect of the invention, the system can be taught torecognise a series of events which are indicative of a majordisturbance. The example was given in the introduction of tapping andstealing oil from the pipeline. One way of dealing with this type ofseries of events is to use a series of state models, represented by thevariable S0 (for initial state in which no event has occurred), S1, S2,etc. When a first one of the events is detected, then the state isswitched from the initial state S0 to a first state S1. Should a secondone of the series of events be initiated within a fixed period of time,the state can be switched to the second state S2. If, on the other hand,no subsequent event is detected then the state can be switched back tothe initial state S0 as it can be assumed that the sequence of events isnot going to occur. Similarly if a third event is detected, then thestate can be switched to S3. A threshold level will be set at a stateSth. An alarm signal can be initiated if the state Sth is reached due tothe sequence of events occurring.

A more detailed analysis of the sequence of events can be carried out byusing Markov models on each of the various events in the sequence andthen initiating the alarm signal if the Markov model indicates aprobable disturbance. Other self-learning systems can be use.

The network shown in FIG. 1 can also be trained to recognise the seriesof events leading to the disturbance, as described above.

The invention claimed is:
 1. An optical fibre sensor system comprising:an optical fibre; a radiation source for launching radiation into theoptical fibre; a detector for detecting back-scattered radiationback-scattered from the optical fibre; and a signal processor connectedto the detector for generating a plurality of values from theback-scattered radiation, the signal processor comprising: a pluralityof first activation cells of a machine learning model, the plurality offirst activation cells connected to the signal processor and configuredto receive a plurality of components of the back-scattered radiation; aplurality of second activation cells of the machine learning model,overlapping subsets of the first activation cells being connected toones of the second activation cells; and an output for summing at leastoutputs from a subset of the plurality of second activation cells toproduce a result indicative of at least one interference with theoptical fibre.
 2. The optical fibre sensor system of claim 1, whereinthe radiation source is configured to launch a series of optical pulses.3. The optical fibre sensor system of claim 1, further comprising amemory for storing a plurality of patterns representative of the atleast one interference with the optical fibre or a sequence ofinterferences with the optical fiber.
 4. The optical fibre sensor systemof claim 1, wherein the first activation cells are configured togenerate a first output at a rest frequency in the absence of a firstinput and at an increased frequency dependent at least partially onsummed first inputs from the detector.
 5. The optical fibre sensorsystem of claim 4, wherein the second activation cells are configured togenerate a second output dependent on summed and weighted ones of thefirst outputs.
 6. The optical fibre sensor system of claim 1, furthercomprising: a plurality of third activation cells arranged in layersincluding a middle layer and further layers, where overlapping subsetsof the second activation cells are connected to ones of the thirdactivation cells arranged in the middle layer and overlapping subsets ofthe third activation cells in the middle layer are connected to ones ofthe third activation cells arranged in at least one of the furtherlayers, the output adapted to sum at least output one from ones of thethird activation cells arranged in the further layers.
 7. The opticalfibre sensor system of claim 6, further configured to feedback betweenthe at least one output of the third activation cells and an input ofthe second activation cells.
 8. The optical fibre sensor system of claim1, wherein adjacent ones of the second plurality of activation cells areconnected so as to change a response of the second plurality ofactivation cells dependent on the output of the adjacent ones of thesecond activation cell.
 9. The optical fibre sensor system of claim 1,wherein the machine learning model is an artificial neural network. 10.A method for determining a location of a disturbance, the methodcomprising: launching radiation into an optical fibre; detectingback-scattered radiation; processing the back-scattered radiation toproduce a plurality of components; passing the plurality of componentsto a plurality of first activation cells within a machine learningmodel; triggering first outputs from the first activation cells; passingthe first outputs to a subset of second activation cells within themachine learning model; triggering second outputs from the subset of thesecond activation cells; summing the second outputs from a plurality ofsubsets of the second activation cells; and calculating, by aprogrammable processor associated with the machine learning model, thelocation of the disturbance based on a change in a frequency of thesummed second outputs.
 11. The method of claim 10, further comprising:passing the second outputs to a subsection of third activation cellsarranged in a middle layer of a plurality of layers of third activationcells; triggering at least one of the third activation cells arranged inthe middle layer to provide third outputs to ones of the thirdactivation cells arranged in further one or more layers; and deducingthe disturbance from summed and weighted ones of third outputs of thethird activation cells.
 12. The method of claim 11, wherein outputs ofat least one of the third activation cells are fed back to inputs of atleast one of the second activation cells.
 13. The method of claim 10,wherein the second outputs decay over time.
 14. The method of claim 10,wherein a second output of at least one of the second activation cellsaffects a second output of at least another one of the second activationcells.
 15. The method of claim 10, wherein the triggering of the secondoutputs has a stochastic component.
 16. The method of claim 10, furthercomprising recognising a sequence of interferences with the opticalfibre, the sequence of interferences including the interference.